Abstract Digital twin (DT) bridge the gap between the real and virtual worlds, enhancing decision-making efficiency by facilitating task offloading in the industrial Internet of Things through comprehensive real-world status information. However, the often-overlooked discrepancies between DT and their real-world counterparts introduce high uncertainty into offloading decisions, potentially leading to unexpected outcomes. To address this issue, we adopt prospect theory to formulate a task offloading decision problem that integrates the behavioral tendencies of system participants, thereby maximizing participants’ utility and providing a realistic approach to managing offloading decisions. We reformulate this NP-hard problem as a potential game and demonstrate the existence of a Nash Equilibrium (NE). The finite improvement property is used to implement a decentralized algorithm that identifies the NE in the potential game as a solution to the offloading problem. Furthermore, we theoretically derive an upper bound on the algorithm’s convergence time. The superiority of our proposed scheme over existing schemes in terms of performance and scalability is evaluated and demonstrated through extensive simulations.

Distributed public ledger (e.g., Blockchain) has been proven to be a powerful technique that allows users to sign transactions in an untrusted environment, where identity-privacy disclosure is gaining attention in practice. Ring signatures can protect identities by providing anonymity property for users. However, a malicious anonymous user may generate multiple signatures on the same transaction, called double-spending attack. A unique ring signature avoids this attack by attaching a unique identifier to the transaction. In addition, future-proof cryptographic solutions are attracting attention in the quantum era. Thus, we aim to propose a post-quantum unique ring signature scheme for anonymous transactions, named . We initially provide verifiable random functions over lattices (L-VRF, in short) with tight security and optimize the proof size (compared with the work of Nguyen et al., ESORICS’ 22) using compression techniques. We then obtain from L-VRF inspired by the previous solution of Franklin-Zhang (FC’ 13) while enables to prevent of quantum computer attacks. Finally, is analyzed under the quantum random oracle model (QROM) while providing a prototype via C language. The performance evaluation shows offers a smaller communication load.

High-definition (HD) map caching at roadside units (RSUs) is an important component of localization for self-driving vehicles, HD map content delivery services must be efficient for various self-driving vehicles. Nevertheless, HD maps are dynamic files that must be updated and replenished in real time. Developing an effective content delivery strategy for different types of self-driving vehicles to require HD maps, while ensuring safe driving and minimizing bandwidth consumption, is challenging. To maximize the monetary utility of the vehicle system, in this article, we jointly optimize the HD map update strategy and the wireless bandwidth resource allocation strategy, considering service delay constraints and overall system risk. However, the optimization problem described above is an NP-hard mixed-integer nonlinear programming (MINLP) problem. Additionally, in a self-driving vehicle scenario, the highly dynamic character of HD maps, the diversity of self-driving vehicle types, and the randomness of vehicle trajectories are unknown to the vehicle system in advance. The intractable optimization problem and the highly uncertain nature of the driving environment make it difficult to find an existing method that allows vehicles to obtain HD maps that meet their localization requirements in a timely manner. To address the above issues, we propose DRL-MURA, which can learn HD map updates and implement a wireless bandwidth resource allocation strategy by constantly interacting with environment based on a deep reinforcement learning (DRL) algorithm. Finally, we prove the accuracy and effectiveness of our method through simulation experiments.

As a promising methodology of distributed Machine Learning (ML) paradigm, Federated Learning (FL) protects data privacy and reduces communication cost by aggregating model parameters rather than raw data. However, training superb FL models incurs a lot of energy consumption, which is a significant challenge for energy-limited Mobile Devices (MDs). To address this challenge, this paper proposes a Wireless Power Transfer (WPT)-aided FL framework, where MDs train local FL models for Base Station (BS) and get corresponding payoff, while Wireless Charge Provider (WCP) provides energy supplement for MDs and charges energy fees. Furthermore, we take into account the time-varying nature of MDs datasets, which affects their energy consumption and reward from BS. Then, we formulate the investigated problem to achieve joint optimization of WPT duration, computing resource allocation and the number of local iterations, with the goal of maximizing the total utility of all MDs throughout the whole FL process. The optimization problem is NP-hard and difficult to be solved by traditional optimization methods within limited timeframes. Therefore, we use Karush-Kuhn-Tucker (KKT) conditions and Lagrange dual method to analyze the problem, and propose a new Improved Lagrangian Subgradient Method (ILSM) as an efficient solution. Finally, extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme under various scenarios, and the results show that the proposed ILSM significantly outperforms other benchmarks in terms of the total utility of all MDs.

Mobile Crowd-Sensing (MCS) has emerged as a significant approach in various domains for collecting and disseminating sensing data. However, it is a challenging issue to select appropriate participants for a sensing task. This paper introduces a Quality Awareness Incentive Mechanism based on Taxation and Data Aggregation (QIM-TDA), which can choose the reliable participants without losing the platform utility and the data quality. Firstly, we define a reputation model to measure the reliability of participant and select more reliable participants based on their reputation in order to maximize platform utility. Secondly, a truth discovery algorithm is proposed to aggregate the sensing data and ensure the data quality of MCS. Finally, a normalized taxation mechanism is discussed in order to prevent the excessive accumulation of reputation for participant and further enhance the data quality. The simulation results prove that QIM-TDA can significantly improve the data quality and task completion rate compared to some typical mechanisms.

The large-scale coverage of LEO satellite networks and the enhancement of onboard satellite computing resources have emerged as a pivotal solution for meeting the computing and routing demands of remote sensing (RS) tasks in areas without ground network coverage. Nonetheles, efficiently leveraging LEO satellite network resources effective computation offloading and relay routing presents substantial challenges. In this study, we delve into the joint optimization of task offloading and routing path selection within LEO satellite networks, aiming to maximize long-term user quality of service while adhering to energy constraints. We propose a novel integrated modeling scheme for relay routing and computation offloading of RS tasks in LEO satellite networks, effectively reducing the scale of system decision problems. Furthermore, we devise a multi-step reward aggregation Deep Q-Learning (DQN-MCAR) algorithm for computing-aware routing, effectively addressing the sequential learning problem among the computing-aware routing decisions of subtasks. Finally, theoretical analysis confirms that our proposed algorithm has lower complexity, and the superiority of our scheme is validated by extensive simulation experiments.

Deep learning (DL) greatly enhances cyber anomaly detection capabilities through effective statistical network characteristic. However, previous methods have not fully addressed two real-world scenario-driven challenges. 1) Frequent node access and disconnection sourced from free-bounded 5G/B5G cyberspace introduce unfamiliar communication behavior patterns, reducing the detection ability of the pre-trained DL model. 2) Low-frequency or sporadic communication behaviors lack stable patterns, posing a challenge for existing AI-driven models, including DL-based detection methods. To address these issues, we propose a cyber anomaly detection framework based on Continuous Temporal Graph (CTG) neural network from a new interaction-centered perspective. The proposed framework refines the concrete information interaction between network entities into the CTG evolution process, thereby naturally incorporating new node access behaviors into feature extraction on CTG neural network. We furthermore present a message aggregation scheme on CTG with fusion of spatio-temporal neighborhood, the actual time distribution and the historical state, thus transforming communication into a more stable pattern for the learning of low-frequency interactions. Extensive experiments on 4 novel datasets, including ToN-IoT, UNSWNB15, CIC-Dark2020, J.P. Morgan payment, demonstrate that our approach outperforms state-of-the-art methods, particularly in detecting new access and low-frequency behaviors.

Multi-access edge computing (MEC) has been booming in recent years, as it promises to fulfill the growing low-latency requirements of applications on large amounts of Internet of Things (IoT) devices. Nevertheless, as latency-sensitive applications tend to become more complicated, existing schemes are too sophisticated, which may result in exceeding the real-time requirements of IoT systems. In this paper, we investigate the task offloading problem for the multi-device multi-edge server IoT system integrated with MEC. Firstly, according to the performance gains obtained by offloading different subtasks, we formalize a system latency minimization problem with energy utilization consideration, which has been proven to be NP-hard. Then, to address it, we propose a heuristic computation offloading scheduling scheme, which offloads appropriate subtasks to edge servers such that the system latency is minimized. Additionally, we theoretically prove the upper and lower boundaries of the system latency. Extensive simulation results corroborate that the proposed algorithm is low-complexity yet effective in decreasing the system latency by 16.15% (and up to 51.18%), improving the energy efficiency of local devices by 8.97% (and up to 21.69%) and shortening the offloading strategy execution time by 96.33% (and up to 98.95%).

Deep learning (DL) greatly enhances binary anomaly detection capabilities through effective statistical network characterization; nevertheless, the intrusion class differentiation performance is still insufficient. Two related challenges have not been fully explored. 1) Statistical attack characteristics are overemphasized while ignoring inherent attack topologies; sequence features are extracted from whole traffic flows, but the interaction evolution of each IP pair over time is rarely considered, such as in long short-term memory (LSTM) and gated recurrent units (GRUs). 2) Meeting the need for many high-quality labeled data samples is an expensive and labor-intensive task in large-scale, complex, and heterogeneous networks. To address these issues, we propose a dynamic line graph neural network (DLGNN)-based intrusion detection method with semisupervised learning. Our model converts network traffic into a series of spatiotemporal graphs. A dynamic GNN (DGNN) is employed to extract spatial information from each discrete snapshot and capture the contextual evolution of communication between IP pairs through consecutive snapshots. Moreover, a line graph realizes edge embedding expressions corresponding to network communications and strengthens the message aggregation ability of graph convolution. Experiments on 6 novel datasets demonstrate that our approach achieves 98.15–99.8% accuracy in abnormality detection with fewer labeled samples. Meanwhile, state-of-the-art multiclass performance is achieved, e.g., the average detection accuracy for DDoS across the 6 datasets reaches 95.32%.

Spatial auditory cues are important for many tasks in immersive virtual environments, especially guidance tasks. However, due to the limited fidelity of spatial sounds rendered by generic Head-Related Transfer Functions (HRTFs), sound localization usually has a limited accuracy, especially in elevation, which can potentially impact the effectiveness of auditory guidance. To address this issue, we explored whether integrating sonification with spatial audio can enhance the perceptions of auditory guidance cues so user performance in auditory guidance tasks can be improved. Specifically, we investigated the effects of sonification mapping strategy using a controlled experiment that compared four elevation sonification mapping strategies: absolute elevation mapping, unsigned relative elevation mapping, signed relative elevation mapping, and binary relative elevation mapping. In addition, we examined whether azimuth sonification mapping can further benefit the perception of spatial sounds. The results demonstrate that spatial auditory cues can be effectively enhanced by integrating elevation and azimuth sonification, where the accuracy and speed of guidance tasks can be significantly improved. In particular, the overall results suggest that binary relative elevation mapping is generally the most effective strategy among four elevation sonification mapping strategies, which indicates that auditory cues with clear directional information are key to efficient auditory guidance.

Considering the advantages of mobile edge computing (MEC), such as low latency, high bandwidth, etc., more and more mobile services are cached to mobile edge servers. However, due to limited computing resources and storage capacity of mobile edge servers, it is hard to guarantee that all services are cached and all computation offloading requests are satisfied. In this paper, we jointly optimize service caching and computation offloading to maximize system profits in mobile edge-cloud computing (MECC). The problem is formalized as a nonconvex optimization problem with discrete variables. We propose a Dynamic Joint computation Offloading and Service Caching algorithm (DJOSC) to solve the problem. Specifically, a regularization technique and Lyapunov optimization theory are used to transform the problem into two subproblems, which are solved by convex optimization techniques. Numerical evaluations show that the maximum system profits can be achieved under different computing resources, storage capacities and bandwidth capacities.

With the rapid development of mobile Internet and urban constructions, high-volume and dynamic indoor communications bring challenges to cellular systems. High penetration loss and deep shadowing channels of indoor users may substantially degrade the transmission efficiency and system throughput. To address this issue, this paper proposes a solution using Floating Relay (FR) given the mature technologies of unmanned aerial vehicle (UAV). We target the undesirable channel conditions of indoor users, introduce the FR into the cellular system to improve transmission efficiency and maximize system throughput. Considering the capacity limit of the FR's back-haul link and the maximum transmission power of each user, an optimization problem is formulated to maximize the system throughput. The optimal power allocation strategy is then obtained for each user, and two effective online 3-D placement algorithms are proposed for the FR to approach the optimal location in the unpredictable and predictable scenarios, respectively. Extensive simulations are conducted. The achieved maximum system throughput, convergence rate, and accumulated throughput are used to evaluate the proposed algorithms. According to the comparisons between the two proposed algorithms and with off-line schemes, they show superiorities in their targeted scenarios, respectively.

Mobile data demand of users is soaring with the increasing number of smartphones, which brings huge challenges to cellular network providers. To meet the user traffic demand, we study the problem that the user traffic data is served by cellular and WiFi network concurrently, i.e., mobile data offloading between cellular and WiFi operators. Different from the existing work where the users or network operators possess the complete information about each other, we model the mobile data offloading problem as a multi-user multi-operator non-cooperative game with the incomplete information. In the proposed model, the users and operators are assumed to be rational, and each of them pursues its own maximum benefit. To address this problem, in a distributed way, we first develop a Marginal Utility-Based Traffic Allocation (MUBTA) algorithm to arrange the users' traffic among different networks. Then, a bidding model is built to adjust the transaction price between users and operators. In addition, a Nash equilibrium is proven to be existed by theoretical analysis. Simulation results show that the proposed approach achieves a near-optimal solution.

Wireless communication via unmanned aerial vehicles (UAVs) is a promising way to provide transmission coverage and computation capacity for mobile user devices, especially in remote areas where the communication resources and infrastructures are extremely limited. In this paper, we study a UAV-assisted traffic and computation offloading (UTCO) problem, where one UAV can provide communication and computation capabilities for its covered user equipments (UEs). The UTCO problem is formulated as a problem of maximizing user satisfaction, in which the bandwidth allocation, transmit power including the UEs and UAV, and UAV trajectory are jointly considered. However, the UTCO problem is proven to be non-convex and some variables are nonlinear coupled, which cause it difficult to be solved optimally. To tackle this challenge, we propose a two-stage alternative optimization approach by leveraging successive convex approximation (SCA) method to obtain a near-optimal solution. The simulation results show that the proposed approach can achieve an outstanding performance in convergence speed and user satisfaction.

The rapid growth of user demand for mobile data traffic, especially video streaming, places a serious burden on base stations. Cache-enabled D2D communication has emerged as a promising paradigm to offload cellular traffic, in which contents are cached at user devices and then shared among neighbors via D2D communications. It is a key step to optimize the content caching policy in the cache-enabled D2D-assisted offloading. To maximize D2D offloading probability, we propose an optimal content caching policy to cache valuable contents by considering both the mode selection and user preference of neighbors. Then we use a dual simplex method to solve the maximum D2D offloading probability problem. Comparing with existing schemes, the simulation results prove the advantage of our proposed scheme in the improvement of D2D offloading performance.

With the widespread adoption of location-based services, users are increasingly demanding high- precision indoor localization. However, the deployment of localization network elements, i.e., localization base stations (LBS), mostly depends on experiences which usually leads to an extreme deployment cost. We therefore develop an indoor localization simulation platform, which can obtain the error distributions of the localization system under different LBS deployments, and also provide a near-optimal LBS deployment in practice.

Device-to-device (D2D) communication is a promising technique for traffic offloading in next-generation cellular systems. In this paper, we study the D2D-assisted cellular traffic offloading (DACTO) problem, where Wi-Fi Direct technology is employed in D2D communication in consideration of its wide communication coverage and high transmission rate. Taking into account the user traffic demands and population distributions, we formulate the DACTO problem as a "Min-Max" problem, in which the operator energy consumption is minimized and meanwhile the user satisfaction is maximized. The DACTO is proven to be a NP-complete problem and is difficult to tackle with the increasing number of population. To achieve a feasible solution, we convert the DACTO problem into an approximate combination optimization problem, and develop a backpack algorithm combined with an improved Hungarian algorithm to solve it. Simulation results show that the proposed method achieves the near-optimal solution for the DACTO problem.

The special characteristics of the mobile environment, such as limited bandwidth, dynamic topology, heterogeneity of peers, and limited power, pose additional challenges on mobile peer-to-peer (MP2P) networks. Trust management becomes an essential component of MP2P networks to promote peer transactions. However, in an MP2P network, peers frequently join and leave the network, which dynamically changes the network topology. Thus, it is difficult to establish long-term and effective trust relationships among peers. In this paper, we propose a dynamic grouping based trust model (DGTM) to classify peers. A group is formed according to the peers’ interests. Within a group, mobile peers share resources and tend to keep stable trust relationships. We propose three peer roles (super peers, relay peers, and ordinary peers) and two novel trust metrics (intragroup trust and intergroup trust). The two metrics are used to accurately measure the trust between two peers from the same group or from different groups. Simulations illustrate that our proposed DGTM always achieves the highest successful transaction rate and the best communication overhead under different circumstances.

Dense network coding (NC) is widely used in wireless cooperative downloading systems. Wireless devices have limited computing resources. Researchers have recently found that dense NC is not suitable because of its high coding complexity, and it is necessary to use chunked NC in wireless environments. However, chunked NC can cause more communications, and the amount of communications is affected by the chunk size. Therefore, setting a suitable chunk size to improve the overall perfor-mance of chunked NC is a prerequisite for applying it in wireless cooperative downloading systems. Most of the existing studies on chunked NC focus on centralized wireless broadcasting systems, which are different from wireless cooperative downloading systems with distributed features. Accordingly, we study the performance of chunked NC based wireless cooperative downloading systems. First, an analysis model is established using a Markov process taking the distributed features into consideration, and then the block collection completion time of encoded blocks for cooperative downloading is optimized based on the analysis model. Furthermore, queuing theory is used to model the decoding process of the chunked NC. Combining queuing theory with the analysis model, the decoding completion time for cooperative downloading is optimized, and the optimal chunk size is derived. Numerical simulation shows that the block collection completion time and the decode completion time can be largely reduced after optimization.

In Distributed Virtualized Environment, service components on a dependable service path will be selected to implement service composition. Searching for the optimal dependable service path is the key to implement dependability assurance, which is a Multi-Constrained Optimal Path problem. However, the existing algorithms have disadvantages of high complexity and low performance, and lacking the consideration of trust relationships and evidence spread among service components during service construction and composition. We proposed the concept of QoD, the Quality of Dependability, introducing some attributes(e.g. component intimacy) to describe and restrict the dependable service path searching in distributed virtualized environment. We also applied Adaptive Bonus-Penalty Micro-canonical Annealing(ABP-MA) to dependable service path searching, and chose service components on the optimal dependable service path to satisfy users' demands for service dependability. The experimental results showed that ABP-MA has the advantages of fast convergence and high search success rate.

Mobile cloud computing (MCC) has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods (such as Monte-Carlo simulation) may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are defined to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.

Future networks devoted much attention to QoS of user experience. This makes it very important to understand the characteristics of user traffic. For network across multiple network layers have the characteristics of varying complexity, put forward a kind of traffic characteristics analysis method based on space and time scales. Firstly, the traffic model is established using multi-scale characterization, and then network behavior at different temporal and spatial scales of structural complexity network behavior is analyzed. Then we explore its change law of time scale, it can success classifies traffic types, so as to accurately forecast the next period of time of business. The results of the experiment data analysis shows that the method can effectively realize online monitoring of the business flow.

In order to inspect the internal and external environment of software efficiently, more and more autonomic computing elements (AEs) are deployed in the autonomic computing architecture software, autonomic software for shorted, which usually causes a great resources waste. Based upon the queuing theory, this paper proposes a new model to process the software internal and external information, which aims at balancing the quantity of the AE and the cost resulted by running this software. The simulated results produced by the designed experiment show that this model has an outstanding capability of rationally assigning the number of AE according to the current internal and external environment of software. Moreover, the efficiency of the algorithm related to proposed model is quite high.

Traditional network configuration managements for users' QoS can achieve well performance to some extent, but all of them could not be applied to the next generation network (NGN) directly, such as integrated services (Intserv) and differentiated services (Diffserv) typically. To solve this issue, we propose a framework of dynamic QoS self-configuration (FDQS) in this paper. Utility function and interruption mechanism are integrated into FDQS where utility function is utilized to express the priorities of users' QoS and interruption mechanism to dynamically correct those priorities during transmission of data packets. Simulation results show that the proposed FDQS holds the same QoS level with Diffserv in well network state, however when network congestion happens, the level of QoS of FDQS is higher than Diffserv almost by 10%.

Autonomic computing has emerged as a paradigm for distributed computing systems to stem the tide of rapidly increasing complexity and evolution problem. In this paper, a two-layer self-reflection model for Autonomic Computing systems (ACs) based on pi-calculus is proposed from a theoretical point of view, which integrates self-awareness and context-awareness into a single model and provides a formal, verifiable basis for the development and further studies of ACs. According to the hierarchical structure, it does not only reduce the latency time of self-awareness in local domain but also gives consideration to the overall objectives of the system. In addition, the model is checked by MWB.